I want to remove all rows (or take all rows without) a question mark symbol in any column. I also want to change the elements to float type.
Input:
X Y Z
0 1 ?
1 2 3
? ? 4
4 4 4
? 2 5
Output:
X Y Z
1 2 3
4 4 4
Preferably using pandas dataframe operations.
You can try first find string ?
in columns, create boolean mask and last filter rows - use boolean indexing. If you need convert columns to float
, use astype
:
print ~((df['X'] == '?' ) (df['Y'] == '?' ) | (df['Z'] == '?' ))
0 False
1 True
2 False
3 True
4 False
dtype: bool
df1 = df[~((df['X'] == '?' ) | (df['Y'] == '?' ) | (df['Z'] == '?' ))].astype(float)
print df1
X Y Z
1 1 2 3
3 4 4 4
print df1.dtypes
X float64
Y float64
Z float64
dtype: object
Or you can try:
df['X'] = pd.to_numeric(df['X'], errors='coerce')
df['Y'] = pd.to_numeric(df['Y'], errors='coerce')
df['Z'] = pd.to_numeric(df['Z'], errors='coerce')
print df
X Y Z
0 0 1 NaN
1 1 2 3
2 NaN NaN 4
3 4 4 4
4 NaN 2 5
print ((df['X'].notnull() ) & (df['Y'].notnull() ) & (df['Z'].notnull() ))
0 False
1 True
2 False
3 True
4 False
dtype: bool
print df[ ((df['X'].notnull() ) & (df['Y'].notnull() ) & (df['Z'].notnull() )) ].astype(float)
X Y Z
1 1 2 3
3 4 4 4
Better is use:
df = df[(df != '?').all(axis=1)]
Or:
df = df[~(df == '?').any(axis=1)]
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